Protein complex structure prediction powered by multiple sequence alignments of interologs from multiple taxonomic ranks and AlphaFold2

Si, Yunda; Yan, Chengfei

doi:10.1093/bib/bbac208

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…Very soon after its release, AlphaFold2 was also tested for its capacity to predict protein-protein interactions. Despite not being designed for this purpose, AlphaFold2 outperformed traditional methods for the structural prediction of complexes between globular protein domains, in terms of both success rate and model quality 21,22,23,24,25,26,27 . AlphaFold-Multimer, specifically retrained on protein complexes, displayed improved performance for interface modeling over the original AlphaFold2 21,22,28 .…”

Section: Introductionmentioning

confidence: 99%

From interaction networks to interfaces: Scanning intrinsically disordered regions using AlphaFold2

Bret

Andréani

Guérois

2023

Preprint

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The revolution brought about by AlphaFold2 and the performance of AlphaFold2-Multimer open promising perspectives to unravel the complexity of protein-protein interaction networks. Nevertheless, the analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered protein regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins involved in the interaction networks, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface. By delineating the interaction region into fragments of decreasing size and combining different strategies for integrating evolutionary information, we managed to raise this success rate up to 90%. Beyond the correct identification of the interaction site, our study also explores specificity issues. We show the advantages and limitations of using the AlphaFold2 confidence score to discriminate between alternative binding partners, a task that can be particularly challenging in the case of small interaction motifs.

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Section: Introductionmentioning

confidence: 99%

From interaction networks to interfaces: Scanning intrinsically disordered regions using AlphaFold2

Bret

Andréani

Guérois

2023

Preprint

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“…For homomeric PPIs, the paired MSA is formed by concatenating two copies of the MSA. For heteromeric PPIs, the paired MSA is formed by pairing the MSAs through the phylogeny-based approach described in https://github.com/ChengfeiYan/PPI_MSA-taxonomy_rank ( Yan, 2024 ; Si and Yan, 2022 ). We input the paired MSA into CCMpred ( Seemayer et al, 2014 ) to get the evolutionary coupling matrix and into alnstats ( Jones et al, 2015 ) to get mutual information matrix, APC-corrected mutual information matrix, and contact potential matrix.…”

Section: Methodsmentioning

confidence: 99%

Protein language model-embedded geometric graphs power inter-protein contact prediction

Si,

Yan

2024

eLife

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Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions (PPIs). Although significant improvement has been made in inter-protein contact prediction recently, there is still large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets illustrates that PLMGraph-Inter outperforms five top inter-protein contact prediction methods, including DeepHomo, GLINTER, CDPred, DeepHomo2 and DRN-1D2D_Inter by large margins. In addition, we also show that the prediction of PLMGraph-Inter can complement the result of AlphaFold-Multimer. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

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“…For homomeric PPIs, the paired MSA is formed by concatenating two copies of the MSA. For heteromeric PPIs, the paired MSA is formed by pairing the MSAs through the phylogeny-based approach described in (https://github.com/ChengfeiYan/PPI_MSA-taxonomy_rank) (Si & Yan, 2022). We input the paired MSA into CCMpred (Seemayer et al, 2014) to get the evolutionary coupling matrix, and into alnstats (Jones et al, 2015) to get mutual information matrix, APC-corrected mutual information matrix and contact potential matrix.…”

Section: D Feature From Paired Msamentioning

confidence: 99%

Protein language model embedded geometric graphs power inter-protein contact prediction

Yan

2023

Preprint

Self Cite

View full text Add to dashboard Cite

Accurate prediction of contacting residue pairs between interacting proteins is very useful for structural characterization of protein-protein interactions (PPIs). Although significant improvement has been made in inter-protein contact prediction recently, there is still large room for improving the prediction accuracy. Here we present a new deep learning method referred to as PLMGraph-Inter for inter-protein contact prediction. Specifically, we employ rotationally and translationally invariant geometric graphs obtained from structures of interacting proteins to integrate multiple protein language models, which are successively transformed by graph encoders formed by geometric vector perceptrons and residual networks formed by dimensional hybrid residual blocks to predict inter-protein contacts. Extensive evaluation on multiple test sets shows that PLMGraph-Inter significantly outperforms three top inter-protein contact prediction methods, including DRN-1D2D_Inter, DeepHomo and GLINTER. Finally, we show leveraging the contacts predicted by PLMGraph-Inter as constraints for protein-protein docking can dramatically improve its performance for protein complex structure prediction.

show abstract

Protein complex structure prediction powered by multiple sequence alignments of interologs from multiple taxonomic ranks and AlphaFold2

Cited by 14 publications

References 28 publications

From interaction networks to interfaces: Scanning intrinsically disordered regions using AlphaFold2

From interaction networks to interfaces: Scanning intrinsically disordered regions using AlphaFold2

Protein language model-embedded geometric graphs power inter-protein contact prediction

Protein language model embedded geometric graphs power inter-protein contact prediction

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